Support Zipformer transducer ASR with whisper features. (#2321)

Adds support for Zipformer transducer ASR models that use Whisper-style 
features by introducing a new feature flag, parsing metadata, 
and integrating per-chunk normalization.

- Introduce UseWhisperFeature in the model interface and Zipformer implementation
- Parse "feature" metadata to set the whisper flag and wire it into the recognizer
- Update feature extraction logic to handle Whisper filterbanks with early returns

@@ -131,10 +131,13 @@ class FeatureExtractor::Impl {
     std::lock_guard<std::mutex> lock(mutex_);
     if (fbank_) {
       fbank_->InputFinished();
+      return;
     } else if (whisper_fbank_) {
       whisper_fbank_->InputFinished();
+      return;
     } else if (mfcc_) {
       mfcc_->InputFinished();
+      return;
     }
 
     SHERPA_ONNX_LOGE("unreachable code");

@@ -16,6 +16,7 @@
 
 #include "sherpa-onnx/csrc/file-utils.h"
 #include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/offline-whisper-model.h"
 #include "sherpa-onnx/csrc/online-lm.h"
 #include "sherpa-onnx/csrc/online-recognizer-impl.h"
 #include "sherpa-onnx/csrc/online-recognizer.h"
@@ -133,6 +134,10 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
                        config.decoding_method.c_str());
       exit(-1);
     }
+
+    if (model_->UseWhisperFeature()) {
+      config_.feat_config.is_whisper = true;
+    }
   }
 
   template <typename Manager>
@@ -182,6 +187,10 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
                        config.decoding_method.c_str());
       exit(-1);
     }
+
+    if (model_->UseWhisperFeature()) {
+      config_.feat_config.is_whisper = true;
+    }
   }
 
   std::unique_ptr<OnlineStream> CreateStream() const override {
@@ -292,6 +301,11 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
       std::vector<float> features =
           ss[i]->GetFrames(num_processed_frames, chunk_size);
 
+      if (config_.feat_config.is_whisper) {
+        OfflineWhisperModel::NormalizeFeatures(features.data(), chunk_size,
+                                               feature_dim);
+      }
+
       // Question: should num_processed_frames include chunk_shift?
       ss[i]->GetNumProcessedFrames() += chunk_shift;
 

@@ -132,6 +132,8 @@ class OnlineTransducerModel {
 
   virtual int32_t SubsamplingFactor() const { return 4; }
 
+  virtual bool UseWhisperFeature() const { return false; }
+
   virtual OrtAllocator *Allocator() = 0;
 
   Ort::Value BuildDecoderInput(

@@ -120,6 +120,12 @@ void OnlineZipformer2TransducerModel::InitEncoder(void *model_data,
   SHERPA_ONNX_READ_META_DATA(T_, "T");
   SHERPA_ONNX_READ_META_DATA(decode_chunk_len_, "decode_chunk_len");
 
+  std::string feature_type;
+  SHERPA_ONNX_READ_META_DATA_STR_WITH_DEFAULT(feature_type, "feature", "");
+  if (feature_type == "whisper") {
+    use_whisper_feature_ = true;
+  }
+
   if (config_.debug) {
     auto print = [](const std::vector<int32_t> &v, const char *name) {
       std::ostringstream os;

@@ -52,6 +52,8 @@ class OnlineZipformer2TransducerModel : public OnlineTransducerModel {
   int32_t VocabSize() const override { return vocab_size_; }
   OrtAllocator *Allocator() override { return allocator_; }
 
+  bool UseWhisperFeature() const override { return use_whisper_feature_; }
+
  private:
   void InitEncoder(void *model_data, size_t model_data_length);
   void InitDecoder(void *model_data, size_t model_data_length);
@@ -103,6 +105,10 @@ class OnlineZipformer2TransducerModel : public OnlineTransducerModel {
   int32_t context_size_ = 0;
   int32_t vocab_size_ = 0;
   int32_t feature_dim_ = 80;
+
+  // for models from
+  // https://github.com/k2-fsa/icefall/blob/master/egs/multi_zh-hans/ASR/RESULTS.md#streaming-with-ctc-head
+  bool use_whisper_feature_ = false;
 };
 
 }  // namespace sherpa_onnx